The GPCR-Gαs-PKA signaling axis promotes T cell dysfunction and cancer immunotherapy failure.

Immune checkpoint blockade (ICB) targeting PD-1 and CTLA-4 has revolutionized cancer treatment. However, many cancers do not respond to ICB, prompting the search for additional strategies to achieve durable responses. G-protein-coupled receptors (GPCRs) are the most intensively studied drug targets but are underexplored in immuno-oncology. Here, we cross-integrated large singe-cell RNA-sequencing datasets from CD8+ T cells covering 19 distinct cancer types and identified an enrichment of Gαs-coupled GPCRs on exhausted CD8+ T cells. These include EP2, EP4, A2AR, ß1AR and ß2AR, all of which promote T cell dysfunction. We also developed transgenic mice expressing a chemogenetic CD8-restricted Gαs-DREADD to activate CD8-restricted Gαs signaling and show that a Gαs-PKA signaling axis promotes CD8+ T cell dysfunction and immunotherapy failure. These data indicate that Gαs-GPCRs are druggable immune checkpoints that might be targeted to enhance the response to ICB immunotherapies.

Genomic analysis of 220 CTCLs identifies a novel recurrent gain-of-function alteration in RLTPR (p.Q575E).

Cutaneous T-cell lymphoma (CTCL) is an incurable non-Hodgkin lymphoma of the skin-homing T cell. In early-stage disease, lesions are limited to the skin, but in later-stage disease, the tumor cells can escape into the blood, the lymph nodes, and at times the visceral organs. To clarify the genomic basis of CTCL, we performed genomic analysis of 220 CTCLs. Our analyses identify 55 putative driver genes, including 17 genes not previously implicated in CTCL. These novel mutations are predicted to affect chromatin (BCOR, KDM6A, SMARCB1, TRRAP), immune surveillance (CD58, RFXAP), MAPK signaling (MAP2K1, NF1), NF-κB signaling (PRKCB, CSNK1A1), PI-3-kinase signaling (PIK3R1, VAV1), RHOA/cytoskeleton remodeling (ARHGEF3), RNA splicing (U2AF1), T-cell receptor signaling (PTPRN2, RLTPR), and T-cell differentiation (RARA). Our analyses identify recurrent mutations in 4 genes not previously identified in cancer. These include CK1α (encoded by CSNK1A1) (p.S27F; p.S27C), PTPRN2 (p.G526E), RARA (p.G303S), and RLTPR (p.Q575E). Last, we functionally validate CSNK1A1 and RLTPR as putative oncogenes. RLTPR encodes a recently described scaffolding protein in the T-cell receptor signaling pathway. We show that RLTPR (p.Q575E) increases binding of RLTPR to downstream components of the NF-κB signaling pathway, selectively upregulates the NF-κB pathway in activated T cells, and ultimately augments T-cell-receptor-dependent production of interleukin 2 by 34-fold. Collectively, our analysis provides novel insights into CTCL pathogenesis and elucidates the landscape of potentially targetable gene mutations.

MICAL2 Is a Super Enhancer Associated Gene that Promotes Pancreatic Cancer Growth and Metastasis.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest solid cancers and thus identifying more effective therapies is a major unmet need. In this study we characterized the super enhancer (SE) landscape of human PDAC to identify novel, potentially targetable, drivers of the disease. Our analysis revealed that MICAL2 is a super enhancer-associated gene in human PDAC. MICAL2 is a flavin monooxygenase that induces actin depolymerization and indirectly promotes SRF transcription by modulating the availability of serum response factor coactivators myocardin related transcription factors (MRTF-A and MRTF-B). We found that MICAL2 is overexpressed in PDAC and correlates with poor patient prognosis. Transcriptional analysis revealed that MICAL2 upregulates KRAS and EMT signaling pathways, contributing to tumor growth and metastasis. In loss and gain of function experiments in human and mouse PDAC cells, we observed that MICAL2 promotes both ERK1/2 and AKT activation. Consistent with its role in actin depolymerization and KRAS signaling, loss of MICAL2 expression also inhibited macropinocytosis. Through in vitro phenotypic analyses, we show that MICAL2, MRTF-A and MRTF-B influence PDAC cell proliferation, migration and promote cell cycle progression. Importantly, we demonstrate that MICAL2 is essential for in vivo tumor growth and metastasis. Interestingly, we find that MRTF-B, but not MRTF-A, phenocopies MICAL2-driven phenotypes in vivo . This study highlights the multiple ways in which MICAL2 impacts PDAC biology and suggests that its inhibition may impede PDAC progression. Our results provide a foundation for future investigations into the role of MICAL2 in PDAC and its potential as a target for therapeutic intervention.

NMFClustering: Accessible NMF-based clustering utilizing GPU acceleration.

Non-negative Matrix Factorization (NME) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using Cupy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePatten gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple 'omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelnes on high performance computing (HPC) culsters that enable reproducible in silco research for non-programmers.

NMF Clustering: Accessible NMF-based Clustering Utilizing GPU Acceleration.

Non-negative Matrix Factorization (NMF) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using CuPy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePattern gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple 'omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelines on high performance computing (HPC) clusters that enable reproducible in silico research for non-programmers.

Single-cell characterization of step-wise acquisition of carboplatin resistance in ovarian cancer.

The molecular underpinnings of acquired resistance to carboplatin are poorly understood and often inconsistent between in vitro modeling studies. After sequential treatment cycles, multiple isogenic clones reached similar levels of resistance, but significant transcriptional heterogeneity. Gene-expression based virtual synchronization of 26,772 single cells from 2 treatment steps and 4 resistant clones was used to evaluate the activity of Hallmark gene sets in proliferative (P) and quiescent (Q) phases. Two behaviors were associated with resistance: (1) broad repression in the P phase observed in all clones in early resistant steps and (2) prevalent induction in Q phase observed in the late treatment step of one clone. Furthermore, the induction of IFNα response in P phase or Wnt-signaling in Q phase were observed in distinct resistant clones. These observations suggest a model of resistance hysteresis, where functional alterations of the P and Q phase states affect the dynamics of the successive transitions between drug exposure and recovery, and prompts for a precise monitoring of single-cell states to develop more effective schedules for, or combination of, chemotherapy treatments.

Defining Key Performance Indicators for the California COVID-19 Exposure Notification System (CA Notify).

OBJECTIVES: Toward common methods for system monitoring and evaluation, we proposed a key performance indicator framework and discussed lessons learned while implementing a statewide exposure notification (EN) system in California during the COVID-19 epidemic. MATERIALS AND METHODS: California deployed the Google Apple Exposure Notification framework, branded CA Notify, on December 10, 2020, to supplement traditional COVID-19 contact tracing programs. For system evaluation, we defined 6 key performance indicators: adoption, retention, sharing of unique codes, identification of potential contacts, behavior change, and impact. We aggregated and analyzed data from December 10, 2020, to July 1, 2021, in compliance with the CA Notify privacy policy. RESULTS: We estimated CA Notify adoption at nearly 11 million smartphone activations during the study period. Among 1 654 201 CA Notify users who received a positive test result for SARS-CoV-2, 446 634 (27%) shared their unique code, leading to ENs for other CA Notify users who were in close proximity to the SARS-CoV-2-positive individual. We identified at least 122 970 CA Notify users as contacts through this process. Contact identification occurred a median of 4 days after symptom onset or specimen collection date of the user who received a positive test result for SARS-CoV-2. PRACTICE IMPLICATIONS: Smartphone-based EN systems are promising new tools to supplement traditional contact tracing and public health interventions, particularly when efficient scaling is not feasible for other approaches. Methods to collect and interpret appropriate measures of system performance must be refined while maintaining trust and privacy.

KITlow Cells Mediate Imatinib Resistance in Gastrointestinal Stromal Tumor.

Gastrointestinal stromal tumor (GIST) is commonly driven by oncogenic KIT mutations that are effectively targeted by imatinib (IM), a tyrosine kinase inhibitor (TKI). However, IM does not cure GIST, and adjuvant therapy only delays recurrence in high-risk tumors. We hypothesized that GIST contains cells with primary IM resistance that may represent a reservoir for disease persistence. Here, we report a subpopulation of CD34+KITlow human GIST cells that have intrinsic IM resistance. These cells possess cancer stem cell-like expression profiles and behavior, including self-renewal and differentiation into CD34+KIThigh progeny that are sensitive to IM treatment. We also found that TKI treatment of GIST cell lines led to induction of stem cell-associated transcription factors (OCT4 and NANOG) and concomitant enrichment of the CD34+KITlow cell population. Using a data-driven approach, we constructed a transcriptomic-oncogenic map (Onco-GPS) based on the gene expression of 134 GIST samples to define pathway activation during GIST tumorigenesis. Tumors with low KIT expression had overexpression of cancer stem cell gene signatures consistent with our in vitro findings. Additionally, these tumors had activation of the Gas6/AXL pathway and NF-κB signaling gene signatures. We evaluated these targets in vitro and found that primary IM-resistant GIST cells were effectively targeted with either single-agent bemcentinib (AXL inhibitor) or bardoxolone (NF-κB inhibitor), as well as with either agent in combination with IM. Collectively, these findings suggest that CD34+KITlow cells represent a distinct, but targetable, subpopulation in human GIST that may represent a novel mechanism of primary TKI resistance, as well as a target for overcoming disease persistence following TKI therapy.

Model-based assessment of mammalian cell metabolic functionalities using omics data.

Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org-CellFie).

Genomic landscape of cutaneous follicular lymphomas reveals 2 subgroups with clinically predictive molecular features.

Primary cutaneous follicle center lymphomas (PCFCLs) are indolent B-cell lymphomas that predominantly remain skin restricted and manageable with skin-directed therapy. Conversely, secondary cutaneous involvement by usual systemic follicular lymphoma (secondary cutaneous follicular lymphoma [SCFL]) has a worse prognosis and often necessitates systemic therapy. Unfortunately, no histopathologic or genetic features reliably differentiate PCFCL from SCFL at diagnosis. Imaging may miss low-burden internal disease in some cases of SCFLs, leading to misclassification as PCFCL. Whereas usual systemic FL is well characterized genetically, the genomic landscapes of PCFCL and SCFL are unknown. Herein, we analyzed clinicopathologic and immunophenotypic data from 30 cases of PCFCL and 10 of SCFL and performed whole-exome sequencing on 18 specimens of PCFCL and 6 of SCFL. During a median follow-up of 7 years, 26 (87%) of the PCFCLs remained skin restricted. In the remaining 4 cases, systemic disease developed within 3 years of diagnosis. Although the SCFLs universally expressed BCL2 and had BCL2 rearrangements, 73% of the PCFCLs lacked BCL2 expression, and only 8% of skin-restricted PCFCLs had BCL2 rearrangements. SCFLs showed low proliferation fractions, whereas 75% of PCFCLs had proliferation fractions >30%. Of the SCFLs, 67% had characteristic loss-of-function CREBBP or KMT2D mutations vs none in skin-restricted PCFCL. Both SCFL and skin-restricted PCFCL showed frequent TNFRSF14 loss-of-function mutations and copy number loss at chromosome 1p36. These data together establish PCFCL as a unique entity with biological features distinct from usual systemic FL and SCFL. We propose 3 criteria based on BCL2 rearrangement, chromatin-modifying gene mutations (CREBBP, KMT2D, EZH2, and EP300), and proliferation index to classify cutaneous FL specimens based on the likelihood of concurrent or future systemic spread.

An accessible, interactive GenePattern Notebook for analysis and exploration of single-cell transcriptomic data.

Single-cell RNA sequencing (scRNA-seq) has emerged as a popular method to profile gene expression at the resolution of individual cells. While there have been methods and software specifically developed to analyze scRNA-seq data, they are most accessible to users who program. We have created a scRNA-seq clustering analysis GenePattern Notebook that provides an interactive, easy-to-use interface for data analysis and exploration of scRNA-Seq data, without the need to write or view any code. The notebook provides a standard scRNA-seq analysis workflow for pre-processing data, identification of sub-populations of cells by clustering, and exploration of biomarkers to characterize heterogeneous cell populations and delineate cell types.

Distinct Patterns of Acral Melanoma Based on Site and Relative Sun Exposure.

Acral melanoma is distinct from melanoma of other cutaneous sites, yet there is considerable variation within this category. To better define this variation, we assessed melanomas occurring on dorsal (n = 21), volar (n = 9), and subungual/interdigital (n = 13) acral skin as well as acral nevi (n = 24) for clinical, histologic, and molecular features. Melanomas on dorsal acral surfaces demonstrated clear differences compared with volar and subungual/interdigital melanomas. The latter two groups exhibited significantly less frequent BRAF mutations (P = 0.01), were significantly less likely to have the superficial spreading histologic subtype (P = 0.01), occurred in older patients (P = 0.05), and had more frequent involvement in non-Caucasians (P = 0.01). These differences can be explained by differing levels of UV exposure. Subungual/interdigital melanomas had the most diverse group of oncogenic mutations including PIK3CA (2/13), STK11 (2/13), EGFR (1/13), FGFR3 (1/13), and PTPN11 (1/13). In addition, subungual/interdigital melanomas had a significantly higher frequency of copy number aberrations (67%) than other subgroups (P = 0.02), particularly in CDK4 and cyclin D1, and were less likely to have BRAF mutations or a superficial spreading histologic subtype (P = 0.05) compared with volar acral melanomas. Although based on a limited sample size, differences between volar and subungual/interdigital melanomas in our study may be the result of differing levels of UV exposure.

Nonoverlapping Clinical and Mutational Patterns in Melanomas from the Female Genital Tract and Atypical Genital Nevi.

Genital melanomas (GM) are the second most common cancer of the female external genitalia and may be confused with atypical genital nevi (AGN), which exhibit atypical histological features but have benign behavior. In this study, we compared the clinical, histological, and molecular features of 19 GM and 25 AGN. We described chromosomal copy number aberrations and the mutational status of 50 oncogenes and tumor suppressor genes in both groups. Our study showed that a pigmented lesion occurring in mucosal tissue, particularly in postmenopausal women, was more likely to be a melanoma than a nevus. GM had high levels of chromosomal instability, with many copy number aberrations. Furthermore, we found a completely nonoverlapping pattern of oncogenic mutations when comparing GM and AGN. In GM, we report somatic mutations in KIT and TP53. Conversely, AGN had frequent BRAF V600E mutations, which were not seen in any of the GM. Our results show that GM and AGN have distinct clinical and molecular changes and that GM have a different mutational pattern compared with AGN.